Clause analysis based on collection coherence in legal domain

ABSTRACT

A method is provided for clause analysis in a legal domain. The method builds a coherence graph from a set of labeled training documents by (a) creating entity nodes from and of a same type as entities extracted from the set of labeled training documents, (b) creating clause nodes from labeled clauses in the set of labeled training documents, (c) forming bi-directional edges (i) between each of the clause nodes and the entity nodes belonging thereto, (ii) among parent-child clause nodes from among the clause nodes, and (iii) among same-level sibling clause nodes from among the clause nodes. The method merges nodes, from among the entity and clause nodes, that have a same semantic meaning. The method weights the bi-directional edges using a coherence metric. The method identifies a clause structure of a new document by matching the new document against the coherence graph using a node-covering algorithm.

BACKGROUND Technical Field

The present invention generally relates to data processing, and moreparticularly to clause analysis based on collection coherence in thelegal domain.

Description of the Related Art

Portable Document Format (PDF) is the de facto format for saving legaldocuments such as, for example, regulations, contracts, masteragreements, statements of work, supplements, and so forth. Texts in PDFfiles are perceived as pictures and are, thus, hard to analyze. Forexample, lines in PDF files are not equal to conventional paragraphs,and so forth. Accordingly, applications need to parse PDF files intostructured information for better governance, understanding,augmentation, and compliance checking. To that end, clause analysis is afundamental parsing process. However, conventional clause analysistechniques suffer from being prone to various types of errors relatingto sequencing characters, grouping characters into tokens, groupingtokens into characters, using multiple columns, left-right versusright-left reading order, using special characters, using fonts withirregular spacing, and using pages with rotated text and/or rotatedpages. Accordingly, there is a need for an improved approach for clauseanalysis in documents such as, but not limited to, legal documents.

SUMMARY

According to an aspect of the present invention, a computer-implementedmethod is provided for clause analysis in a legal domain. The methodincludes building, by a processor device, a coherence graph from a setof labeled training documents by (a) creating entity nodes from and of asame type as entities extracted from the set of labeled trainingdocuments, (b) creating clause nodes from labeled clauses in the set oflabeled training documents, (c) forming bi-directional edges (i) betweeneach of the clause nodes and respective ones of the entity nodesbelonging thereto, (ii) among parent-child clause nodes from among theclause nodes, and (iii) among same-level sibling clause nodes from amongthe clause nodes. The method further includes merging, by the processordevice, nodes, from among the entity nodes and the clause nodes for theset of labeled training documents, that have a same semantic meaning.The method also includes weighting, by the processor device, thebi-directional edges using a coherence metric. The method additionallyincludes identifying, by the processor device, a clause structure of anew document by matching the new document against the coherence graphusing a node-covering algorithm.

According to another aspect of the present invention, a computer programproduct is provided for clause analysis in a legal domain. The computerprogram product includes a non-transitory computer readable storagemedium having program instructions embodied therewith. The programinstructions are executable by a computer to cause the computer toperform a method. The method includes building, by a processor device ofthe computer, a coherence graph from a set of labeled training documentsby (a) creating entity nodes from and of a same type as entitiesextracted from the set of labeled training documents, (b) creatingclause nodes from labeled clauses in the set of labeled trainingdocuments, (c) forming bi-directional edges (i) between each of theclause nodes and respective ones of the entity nodes belonging thereto,(ii) among parent-child clause nodes from among the clause nodes, and(iii) among same-level sibling clause nodes from among the clause nodes.The method further includes merging, by the processor device, nodes,from among the entity nodes and the clause nodes for the set of labeledtraining documents, that have a same semantic meaning. The method alsoincludes weighting, by the processor device, the bi-directional edgesusing a coherence metric. The method additionally includes identifying,by the processor device, a clause structure of a new document bymatching the new document against the coherence graph using anode-covering algorithm.

According to yet another aspect of the present invention, a computerprocessing system is provided for clause analysis in a legal domain. Thesystem includes a memory for storing program code. The system furtherincludes a processor device for running the program code to build acoherence graph from a set of labeled training documents by (a) creatingentity nodes from and of a same type as entities extracted from the setof labeled training documents, (b) creating clause nodes from labeledclauses in the set of labeled training documents, (c) formingbi-directional edges (i) between each of the clause nodes and respectiveones of the entity nodes belonging thereto, (ii) among parent-childclause nodes from among the clause nodes, and (iii) among same-levelsibling clause nodes from among the clause nodes. The processor furtherruns the program code to merge nodes, from among the entity nodes andthe clause nodes for the set of labeled training documents, that have asame semantic meaning. The processor also runs the program code toweight the bi-directional edges using a coherence metric. The processoradditionally runs the program code to identify a clause structure of anew document by matching the new document against the coherence graphusing a node-covering algorithm.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 is a block diagram showing an exemplary processing system towhich the present invention may be applied, in accordance with anembodiment of the present invention;

FIGS. 2-3 are flow diagrams showing an exemplary method for clauseanalysis based on collection coherence in the legal domain, inaccordance with an embodiment of the present invention;

FIG. 4 is a block diagram graphically showing an exemplary method forbuilding a coherence graph with a labeled corpus, in accordance with anembodiment of the present invention;

FIG. 5 is a block diagram showing exemplary clause nodes and theirlinkage, in accordance with an embodiment of the present invention;

FIG. 6 is a block diagram showing an exemplary node creation process, inaccordance with an embodiment of the present invention;

FIG. 7 is a block diagram showing an exemplary node merge process, inaccordance with an embodiment of the present invention;

FIG. 8 is a block diagram showing an exemplary edge creation process, inaccordance with an embodiment of the present invention;

FIG. 9 is a block diagram showing an illustrative cloud computingenvironment having one or more cloud computing nodes with which localcomputing devices used by cloud consumers communicate, in accordancewith an embodiment of the present invention; and

FIG. 10 is a block diagram showing a set of functional abstractionlayers provided by a cloud computing environment, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION

The present invention is directed to clause analysis based on collectioncoherence in the legal domain.

In an embodiment, the present invention includes a graph building stageand an applying (inference) stage. A coherence graph is built during thegraph building stage using a set of labeled documents. During the graphbuilding stage, for each clause in a document, the clause's title andbody are linked together, with all its sub-clauses, and sibling-clauses'titles, and so forth, in order to build the coherence graph. During theinference stage, a new document is compared against the coherence graphin order to identify the clause structure of the new document. Variousactions can be selectively performed responsive to the identified clausestructure and the implementation. These and other features of thepresent invention are described in further detail hereinbelow.

FIG. 1 is a block diagram showing an exemplary processing system 100 towhich the present invention may be applied, in accordance with anembodiment of the present invention. The processing system 100 includesa set of processing units (e.g., CPUs) 101, a set of GPUs 102, a set ofmemory devices 103, a set of communication devices 104, and set ofperipherals 105. The CPUs 101 can be single or multi-core CPUs. The GPUs102 can be single or multi-core GPUs. The one or more memory devices 103can include caches, RAMs, ROMs, and other memories (flash, optical,magnetic, etc.). The communication devices 104 can include wirelessand/or wired communication devices (e.g., network (e.g., WIFI, etc.)adapters, etc.). The peripherals 105 can include a display device, auser input device, a printer, and so forth. Elements of processingsystem 100 are connected by one or more buses or networks (collectivelydenoted by the figure reference numeral 110).

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. Further, in another embodiment, acloud configuration can be used (e.g., see FIGS. 9-10). These and othervariations of the processing system 100 are readily contemplated by oneof ordinary skill in the art given the teachings of the presentinvention provided herein.

Moreover, it is to be appreciated that various figures as describedbelow with respect to various elements and steps relating to the presentinvention that may be implemented, in whole or in part, by one or moreof the elements of system 100.

FIGS. 2-3 are flow diagrams showing an exemplary method 200 for clauseanalysis based on collection coherence in the legal domain, inaccordance with an embodiment of the present invention.

Method 200 can be considered to involve two stages, namely a “graphbuilding time stage” 291 and a “graph applying time stage” 292corresponding to a graph building time and a graph applying time,respectively. The graph applying time stage 292 is also interchangeablyreferred to herein as the “inference stage” 292.

At block 205, commence a loop for each document. Block 205 correspondsto the start of the graph building time stage 291.

At block 210, extract entities from a current document being processedand represent the entities by nodes. The entities can include, but arenot limited to, name, quantity, quality, date, state, and action. Thecorresponding representations for the aforementioned entities canrespectively be name nodes, quantity nodes, quality nodes, date nodes,state nodes, and action nodes. As can be seen, in an embodiment, therespective representations of the entities can be the entity type. Theseand other representation can be used, as readily appreciated by one ofordinary skill in the art given the teachings of the present inventionprovided herein, while maintaining the spirit of the present invention.

At block 215, extract clauses from the current document being processedand represent the clauses by clause nodes.

At block 220, build edges (i) between clause nodes and entity nodes,(ii) among parent-child clause nodes, and (iii) among sibling clausenodes.

At block 225, end the loop for each document.

At block 230, commence a loop among all documents.

At block 235, merge nodes and weigh the edges by a coherence metric. Itis to be appreciated that a result of block 235 can be groups ofdocuments having a strong coherence with respect to each other, based onthe coherence metric. For example, different ranges of the coherencemetric can represent different document groups.

At block 240, end the loop among all documents. Block 240 corresponds tothe end of the graph building time stage 291.

At block 245, receive a new document. Block 245 corresponds to the startof the graph applying time stage 292.

At block 250, identify a clause structure of the new document, bymatching the lines of the new document against the coherence graph usinga best node-covering algorithm. Block 250 corresponds to the end of thegraph applying time stage 292.

At block 255, perform an action responsive to the clause structure.

Further regarding block 255, the action that is performed is dependentupon the particular implementation. For example, an one exemplaryaction, the clause structure, once identified, could be modified into adifferent clause structure. For example, the identified clause structurecould be a non-compliant clause structure, such that the action involveschanging the identified non-compliant clause structure into a compliantclause structure. This can prevent a document initially having anincompatible clause structure from being used until the document ismodified to be made compliant with a particular set of requirements. Theset of requirements can involve processing resources, format, processingtime, and so forth. Moreover, optimized formats can be substituted inplace of otherwise suitable but slower processed formats, where theoptimized formats are designed for quick processing so as to minimizecomputational and processing resources implicated in processingdocuments having such optimized formats. The optimized format designedfor quick processing can involve special characters or statement/clausestructures that are designed to be more readily (i.e., quicker)recognized and/or more readily (quicker) parsed and/or so forth. Asanother example, omission detection can be performed at the clause levelin order to detect omissions of expected and likely important clauses indocuments in the legal domain. In such a case, the action can be thedetection of an omission and the inclusion of the omitted material as anew clause in the new document. In this way, completeness of legaldocuments can be enhanced if not assured. These and other actions arereadily determined by one of ordinary skill in the art, given theteachings of the present invention provided herein, while maintainingthe spirit of the present invention.

FIG. 4 is a block diagram graphically showing an exemplary method 400for building a coherence graph with a labelled corpus, in accordancewith an embodiment of the present invention. Method 400 can beconsidered to correspond to the graph building time stage 291.

At block 405, input a labelled corpus 481 that includes annotated legaldocuments.

At block 410, extract entities from the labelled corpus 481. Theentities can be clause nodes and entity nodes 482. For example, ClauseCi can be represented as follows: <buyer> <payment amount> <pay beforeexecution>.

At block 415, create nodes (from the entities).

At block 420, merge nodes with a coherence metric.

At block 425, link parent-child clauses.

At block 430, link sibling clauses.

At block 435, weigh edges.

FIG. 5 is a block diagram showing exemplary clause nodes 510, an entitynode 520, and their linkage 530, in accordance with an embodiment of thepresent invention. Each of the clause nodes is represented by the letter“C” followed by a respective integer, each of the entity nodes isrepresented by a respective filled-in circle, and each of the edges isrepresented by a respective line from one node to another node.

The clause nodes 510 include nodes C1, C2, C3, and C4.

In this example, the entity nodes 520 include a single node as shown bythe filled in circle.

The linkage 530 includes edge that link the clause and entity nodes.

FIG. 6 is a block diagram showing an exemplary node creation process600, in accordance with an embodiment of the present invention.

The node creation process 600 involves the following nodes:

-   (A) Clause node: payment;-   (B) Action node: pay;-   (C) Name node: foreign exchange rate;-   (D) Quantity node: 1 U.S. dollar;

(E) Action node: review base rate;

(F) Name node: Federal Reserve; and

(G) Date node: evaluation period.

FIG. 7 is a block diagram showing an exemplary node merge process 700,in accordance with an embodiment of the present invention.

In the node merge process 700, the involved node types include thefollowing: clause; action; name; quantity; and date.

In the node merge process 700, nodes of the same type and coherentmeaning are merged.

FIG. 8 is a block diagram showing an exemplary edge creation process700, in accordance with an embodiment of the present invention.

In the edge creation process 800, the following apples:

-   (i) create bi-directional “clause-entity” edge between clause node    and its belonging entity nodes;-   (ii) create “parent-child” edge between a clause node and its    sub-clause nodes; and-   (iii) create bi-directional “sibling” edge between adjacent clauses    of same levels.

In the edge creation process 800, the involved clause nodes are C1, C2,C3, C4, C5, and C6.

In the edge creation process 800, the following (non-clause) nodes areinvolved, with respect to clause node C2:

-   (B) Action node: pay;-   (C) Name node: foreign exchange rate;-   (D) Quantity node: 1 U.S. dollar; and-   (G) Date node: evaluation period.

A description will now be given regarding edge weighting, in accordancewith an embodiment of the present invention.

In an embodiment, an empirical approach can be used as follows:

weight(e)=f(types of connected nodes, occurrence)

In an embodiment, a machine learning approach can be used. In such acase, the input can include, for example: labeled documents, includinglines of text, extracted entity nodes, and clause nodes. Moreover, theobjective function can be to maximize the sum score resultant from anode covering algorithm. A vertex cover, aka node cover, of a graph is aset of vertices such that each edge of the graph is incident to at leastone vertex in the set.

In an embodiment, method 200 can be provided as a cloud service in orderto provide cloud-based document compliance checking. The service couldfurther offer actions that can be performed depending upon the resultsof checking a given document. These and other implementations of method200, including cloud and non-cloud-based implementations, are readilydetermined by one of ordinary skill in the art, given the teachings ofthe present invention provided herein, while maintaining the spirit ofthe present invention.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 9, illustrative cloud computing environment 950 isdepicted. As shown, cloud computing environment 950 includes one or morecloud computing nodes 910 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 954A, desktop computer 954B, laptop computer 954C,and/or automobile computer system 954N may communicate. Nodes 910 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 950 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 954A-Nshown in FIG. 9 are intended to be illustrative only and that computingnodes 910 and cloud computing environment 950 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 10, a set of functional abstraction layersprovided by cloud computing environment 950 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 1060 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1061;RISC (Reduced Instruction Set Computer) architecture based servers 1062;servers 1063; blade servers 1064; storage devices 1065; and networks andnetworking components 1066. In some embodiments, software componentsinclude network application server software 1067 and database software1068.

Virtualization layer 1070 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1071; virtual storage 1072; virtual networks 1073, including virtualprivate networks; virtual applications and operating systems 1074; andvirtual clients 1075.

In one example, management layer 1080 may provide the functionsdescribed below. Resource provisioning 1081 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1082provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1083 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1084provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1085 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1090 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1091; software development and lifecycle management 1092;virtual classroom education delivery 1093; data analytics processing1094; transaction processing 1095; and clause analysis based oncollection coherence in the legal domain 1096.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A computer-implemented method for clause analysisin a legal domain, the method comprising: building, by a processordevice, a coherence graph from a set of labeled training documents by(a) creating entity nodes from and of a same type as entities extractedfrom the set of labeled training documents, (b) creating clause nodesfrom labeled clauses in the set of labeled training documents, (c)forming bi-directional edges (i) between each of the clause nodes andrespective ones of the entity nodes belonging thereto, (ii) amongparent-child clause nodes from among the clause nodes, and (iii) amongsame-level sibling clause nodes from among the clause nodes; merging, bythe processor device, nodes, from among the entity nodes and the clausenodes for the set of labeled training documents, that have a samesemantic meaning; weighting, by the processor device, the bi-directionaledges using a coherence metric; and identifying, by the processordevice, a clause structure of a new document by matching the newdocument against the coherence graph using a node-covering algorithm. 2.The computer implemented method of claim 1, wherein the entities areselected from the group consisting of a name, a quantity, a quality, adate, a state, and an action.
 3. The computer-implemented method ofclaim 1, wherein the clause nodes are created from and have at least asame partial title as corresponding ones of the labeled clause nodesextracted from the set of labeled training documents.
 4. Thecomputer-implemented method of claim 1, wherein two of the entity nodeshave the same semantic meaning, and are merged together, when the two ofthe entity nodes are of a same type.
 5. The computer-implemented methodof claim 1, wherein two of the clause nodes have the same semanticmeaning, and are merged together, when the two of the clause nodes havea same stemmed title.
 6. The computer-implemented method of claim 1,wherein said weighting step comprises an empirical-based approach thatweights the edges as a function of connected node types
 7. Thecomputer-implemented method of claim 1, wherein said weighting stepcomprises a machine learning-based approach having an objective functionthat maximizes a sum score of a node covering algorithm.
 8. Thecomputer-implemented method of claim 1, further comprising determiningwhether the clause structure of the new document is compliant with a setof compatibility requirements, and modifying the clause structure of thenew document from a non-compliant clause structure to a compliant clausestructure responsive to determining a lack of compliance by saiddetermining step.
 9. The computer-implemented method of claim 1, whereinthe set of labeled documents comprise legal domain documents.
 10. Acomputer program product for clause analysis in a legal domain, thecomputer program product comprising a non-transitory computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a computer to cause the computer toperform a method comprising: building, by a processor device of thecomputer, a coherence graph from a set of labeled training documents by(a) creating entity nodes from and of a same type as entities extractedfrom the set of labeled training documents, (b) creating clause nodesfrom labeled clauses in the set of labeled training documents, (c)forming bi-directional edges (i) between each of the clause nodes andrespective ones of the entity nodes belonging thereto, (ii) amongparent-child clause nodes from among the clause nodes, and (iii) amongsame-level sibling clause nodes from among the clause nodes; merging, bythe processor device, nodes, from among the entity nodes and the clausenodes for the set of labeled training documents, that have a samesemantic meaning; weighting, by the processor device, the bi-directionaledges using a coherence metric; and identifying, by the processordevice, a clause structure of a new document by matching the newdocument against the coherence graph using a node-covering algorithm.11. The computer program product of claim 10, wherein the entities areselected from the group consisting of a name, a quantity, a quality, adate, a state, and an action.
 12. The computer program product of claim10, wherein the clause nodes are created from and have at least a samepartial title as corresponding ones of the labeled clause nodesextracted from the set of labeled training documents.
 13. The computerprogram product of claim 10, wherein two of the entity nodes have thesame semantic meaning, and are merged together, when the two of theentity nodes are of a same type.
 14. The computer program product ofclaim 10, wherein two of the clause nodes have the same semanticmeaning, and are merged together, when the two of the clause nodes havea same stemmed title.
 15. The computer program product of claim 1,wherein said weighting step comprises an empirical-based approach thatweights the edges as a function of connected node types
 16. The computerprogram product of claim 10, wherein said weighting step comprises amachine learning-based approach having an objective function thatmaximizes a sum score of a node covering algorithm.
 17. The computerprogram product of claim 10, wherein the method further comprisesdetermining whether the clause structure of the new document iscompliant with a set of compatibility requirements, and modifying theclause structure of the new document from a non-compliant clausestructure to a compliant clause structure responsive to determining alack of compliance by said determining step.
 18. The computer programproduct of claim 10, wherein the set of labeled documents comprise legaldomain documents.
 19. A computer processing system for clause analysisin a legal domain, the system comprising: a memory for storing programcode; and a processor device for running the program code to build acoherence graph from a set of labeled training documents by (a) creatingentity nodes from and of a same type as entities extracted from the setof labeled training documents, (b) creating clause nodes from labeledclauses in the set of labeled training documents, (c) formingbi-directional edges (i) between each of the clause nodes and respectiveones of the entity nodes belonging thereto, (ii) among parent-childclause nodes from among the clause nodes, and (iii) among same-levelsibling clause nodes from among the clause nodes; merge nodes, fromamong the entity nodes and the clause nodes for the set of labeledtraining documents, that have a same semantic meaning; weight thebi-directional edges using a coherence metric; and identify a clausestructure of a new document by matching the new document against thecoherence graph using a node-covering algorithm.
 20. The computerprocessing system of claim 19, wherein the clause nodes are created fromand have at least a same partial title as corresponding ones of thelabeled clause nodes extracted from the set of labeled trainingdocuments.